[visionlist] Journal of Vision - Special Issue - Finding Visual Features - Second Call for Papers

[Journal of Vision]

Journal of Vision  -  Special Issue  -  Finding Visual Features  -  Second Call for Papers

In order to secure the most complete set of contributions, we are extending the deadline for the following special issue.

Finding visual features:
Using stochastic stimuli to discover internal representations
 
In recent years psychophysicists have developed techniques for correlating noisy stimuli with behavioral decisions ('classification images') while neuroscientists have developed related techniques for correlating noisy stimuli with neural responses ('reverse correlation'), both in an effort to infer the internal features that mediate perception. At the same time, cognitive scientists have been testing mathematical models of how observers make categorical decisions about noisy stimuli. And in parallel, computer scientists, mathematicians, and statisticians have been developing efficient algorithms and optimal methods for classifying noisy targets into categories defined by humans, and for identifying the stimulus properties that evoke neural responses. In December, 2004, a Neural Information Processing Society (NIPS) workshop brought together researchers from these fields to share and discuss these diverse approaches to solving their common problem of feature induction.

This special issue of the Journal of Vision is designed to allow full expression of these ideas, and to encourage additional work in this area. We invite articles on the general theme of induction of internal features from noisy response data. We emphasize that contributors are not restricted to participants at the NIPS 2004 workshop. We encourage submission of original research, reviews, and theoretical commentaries. 

Appropriate topics include, but are not limited to:

	-Higher-order and non-linear classification image techniques
	-Neural reverse correlation techniques to extract higher-order kernels
	-Computational algorithms to infer features from noisy classification data
	-Methods for using fine-grained behavioral measures (e.g., confidence ratings, reaction times) to improve feature inference

Guest Editors: 

Jason M. Gold     Indiana University, Bloomington  
Richard Shiffrin    Indiana University, Bloomington 
James Elder      York University, Toronto  
Jack Gallant      University of California, Berkeley 

Deadline for submission: September 1, 2005 

Target publication date: December 1, 2005

To date, the following authors have committed to submit an article to this issue:

Craig Abbey
Andrew Cohen
James Danemiller
Miguel Eckstein
James Elder
Jack Gallant
Jason Gold
Norberto Grzywacz
Stanley Klein
Ansgar Koene
Martin Lankheet
Denis Levi
Melanie Lunsford
Jerry Mendel
Arnivan Nandy
Joaquin Rapela
Bernhard Schölkopf
Rich Shiffrin
Eero Simoncelli
Bosco Tjan
Raymond van Ee
Felix Wichmann

Journal of Vision is an open-access online journal that encourages the use of images, color, movies, hyperlinks, demonstrations, original datasets, and other digital enhancements. To submit a paper to this special issue please follow the Instructions for Authors at http://journalofvision.org/info/instructions.pdf.